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Putra Wanda
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INDONESIA
International Journal of Informatics and Computation
Published by Universitas Respati Yogyakarta
ISSN : 26858711     EISSN : 27145263     DOI : 10.35842/ijicom
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Computation (IJICOM) is an international, peer-reviewed, open-access journal, that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of Informatics includes technologies of information and communication as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJICOM aims to be an international platform to exchange novel research results in simulation-based science across all scientific disciplines. It publishes advanced innovative, interdisciplinary research where complex multi-scale, multi-domain problems in science and engineering are solved, integrating sophisticated numerical methods, computation, data, networks, and novel devices. The scope of this journal includes IoT, 5G, Artificial Intelligence, sensor networks, and high-resolution imaging techniques. This new discipline in science combines computational thinking, modern computational methods, devices, and collateral technologies to address problems far beyond the scope of traditional numerical methods
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 61 Documents
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Establising CNN for Network Intrusion Detection: A Comparative Approach M. Hizbul Wathan; Moh. Aziz
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.69

Abstract

Intrusion detection plays an important role in protecting systems from various threats. However, as intrusion techniques become more sophisticated, traditional detection methods have shown limitations in identifying new attacks. This research addresses the pressing issue of improving intrusion detection by utilizing Convolutional Neural Networks (CNN) algorithms, compared to various other machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Trees, and Gradient Boosting (GBoost). The main objective is to evaluate and compare the performance of these algorithms using a comprehensive dataset sourced from Kaggle, which includes 25,192 data and 42 features. Using metrics such as accuracy, precision, recall, and F1-score, the results show a complex pattern in the strengths and weaknesses of each. Surprisingly, CNN achieved exceptional accuracy, raising questions that require further investigation. Notably, KNN stands out as the best-performing machine learning algorithm. Contextualized within existing research, this study advances the understanding of the role of machine learning in intrusion detection, providing valuable insights for practical implementation. The findings reinforce the relevance of adapting to the evolving network threat landscape while raising interesting questions for future research. In conclusion, this research provides a comparative analysis of intrusion detection techniques, offering a basis for making informed decisions to improve network security and mitigate evolving threats.
Detection of Oil Palm Seedling Disease Based on Leaf Images Using the MobileNetV2-CNN Architecture Ego Oktafanda; Adyanata Lubis; Elyandri Prasiwiningrum
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.71

Abstract

This study aims to develop and implement a plant disease detection system for oil palm seedlings based on leaf images using the MobileNetV2 architecture, which is based on Convolutional Neural Networks (CNN). The model was trained using a dataset of oil palm leaf images to detect several types of plant diseases. In the experiments, the applied model showed excellent results, with training accuracy increasing from 79% in the first epoch to 96% in the 15 epoch, and validation accuracy also increasing from 89% to 97%. These results demonstrate that the model can effectively detect plant diseases with good generalization ability on unseen data. With stable loss reduction and continuously improving accuracy, this study proves that the MobileNetV2 architecture can be efficiently used for plant disease detection. The research also highlights the potential integration of the model into an application to provide a practical solution in oil palm plantation management and to support decision-making and improve agricultural outcomes.
Line Crossing Detector System for Real-Time Over-Taking Vehicle Detection Ahmad Nanda Yuma Rafi; Mohamad Yusuf
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.72

Abstract

This study introduces a novel method for detecting overtaking vehicles by integrating Virtual Line Detection with the YOLOv8n algorithm. The objective is to enhance road safety by accurately identifying and tracking vehicles as they overtake, which is crucial for preventing. The research demonstrates the effectiveness of this approach, achieving a detection accuracy rate of 80.95% using line crossing detection techniques. This high level of accuracy underscores the potential of the system to reliably identify overtaking maneuvers in traffic conditions. Furthermore, this innovative method holds promising implications for enhancing safety riding by providing realtime alerts to drivers and preventing infrastructure loss resulting from traffic incidents. Our findings suggest that integrating advanced detection algorithms like YOLOv8n with virtual line detection can be a viable solution for modern traffic safety challenges.
Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet Muhammad Ali Sultan; Christopher Marco Angelo; Muhammad Alkam Alfariz; Dinda Fatimah Kautsarina; Dhani Amanda Putra; Muhammad Sharjil Ashfaq; Hadi Santoso; Genoveva Ferreira Sores
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.80

Abstract

Brain tumors have become a leading cause of mortality worldwide. Detecting and classifying brain tumors accurately at the initial stages is challenging due to their complex and varying structure. In this study, an improved fine-tuned model based on Convolutional Neural Networks (CNN) with ResNet50 and U-Net is proposed. The model works on the publicly available TCGA-LGG and TCIA dataset, which consists of 120 patients. The fine- tuned ResNet50 model outperforms the CNN model in brain tumor classification and detection using MRI images. Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones.
Analysis of the Application of Linear Interpolation and Quadratic Interpolation in Electrical Distribution Performance Siswandari Noertjahjani; Syafiq Abdul Zaki; Aris Kiswanto
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.84

Abstract

The electricity distribution network is an important component that distributes electricity from substations to end consumers. Monitoring voltage across the network is critical, but direct measurement at all points is often impractical due to resource limitations. This research uses linear interpolation and quadratic interpolation to estimate stress at unmeasured points. Voltage measurement data at several points is used as a basis for interpolation. With the help of MATLAB, the voltage at unmeasured points can be estimated quickly and accurately. The results of the interpolation of distance on electricity consumption show that electricity consumption decreases as distance increases. At a distance of 5 km (Ds. Galih), electricity consumption was recorded at 1,237.5 kWh, while at a distance of 15 km (Ds. Triharjo) electricity consumption was reduced to 1,232.5 kWh. Furthermore, at a distance of 25 Km (Wungurejo District), electricity consumption was recorded at 1,227.5 kWh, and at a distance of 35 Km (Tejorejo District) electricity consumption decreased again to 1,222.5 kWh. This method enables more comprehensive monitoring of network performance, helps identify areas that require special attention, and supports decisions in network maintenance and repair. In conclusion, linear interpolation is an effective method for estimating voltages in electrical distribution networks, making an important contribution to improving the reliability and efficiency of electrical power distribution. Assists in monitoring and analyzing the performance of the electricity distribution network, thereby enabling more targeted maintenance and repair actions.
Stock Price Prediction in Indonesia's Mining Sector Using a Hybrid Conv1D-LSTM Model Hamzah
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.85

Abstract

This study presents a novel approach to forecasting stock prices in Indonesia's mining sector by leveraging a hybrid model combining Convolutional Neural Networks (Conv1D) and Long Short-Term Memory (LSTM) networks. Given the volatile nature of stock markets and the specific characteristics of the mining industry, accurate prediction models are essential for investors and analysts. The hybrid Conv1D-LSTM model integrates the feature extraction capabilities of Conv1D with the sequence learning strengths of LSTM, providing a robust framework for time series forecasting.
Fake News Detection in Health Domain Using Transformer Models Sri Hasta Mulyani; Suwarto; Hamzah; R.Nurhadi Wijaya; Rodiyah; Wita Adelia
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.89

Abstract

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication. This study investigates the effectiveness of transformer models, particularly BERT (Bidirectional Encoder Representations from Transformers), in detecting fake news related to health. By leveraging the advanced contextual understanding of BERT, we aim to enhance the accuracy of fake news detection in this critical domain. Our approach involves training the BERT model on a curated dataset of health news articles, followed by rigorous evaluation on its ability to differentiate between genuine and misleading content. The results reveal that the transformer-based model significantly outperforms traditional methods, achieving high accuracy and robust performance metrics. This research underscores the potential of transformer models in combating health misinformation and provides a foundation for future improvements in automated fake news detection systems.
Prediction of Peak Ground Acceleration (PGA) in Java Using Artificial Neural Network Method Sofyan Hadi Rahmawan; Cahyo Crysdian; Sri Harini
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.90

Abstract

Java is one of the islands in Indonesia that frequently experiences earthquakes. Earthquakes can cause significant ground motion that can damage buildings and threaten human life. Peak Ground Acceleration (PGA) is a measure of the maximum ground acceleration that occurs during an earthquake and is an important factor that must be considered at every construction site to assess the potential damage that can be caused by an earthquake. The parameters considered in determining PGA predictions are earthquake parameters, such as magnitude and hypocenter distance. In addition, the PGA value is also influenced by local site conditions. With advances in information technology and artificial intelligence, especially in the development of Artificial Neural Networks (ANN), research on PGA prediction needs to be conducted as one of the efforts in reducing the risk of earthquakes. The purpose of this research is to obtain the best network architecture in predicting PGA values. The criteria for selecting the best network architecture is done by comparing the error value of each possible architecture formed. The best prediction results are obtained in the model with 3-15-1 architecture with a correlation value of 0.67.
Comparative Analysis of K-Means and K-Medoids Algorithms in New Student Admission Tikaridha Hardiani; Esi Putri Silmina
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.91

Abstract

Universitas ‘Aisyiyah Yogyakarta is one of the private universities in Yogyakarta. The large number of private universities in Yogyakarta has intensified the competition for new student admissions. In this situation, every university requires the right strategy to attract prospective students. One of the strategies used by Universitas ‘Aisyiyah Yogyakarta to capture the interest of potential students is by conducting direct promotions to schools in Yogyakarta, Java, and Sumatra. In the admission process for new students in the Information Technology Study Program, a common problem arises, which is the number of prospective students who do not complete re-registration each year. These students pass the selection and are declared accepted, but they do not proceed with re-registration. The school presentation strategy contributes to student admissions, making it a good strategy, but it requires significant operational costs. Promotion area segmentation is needed so that this strategy can be more targeted, resulting in more efficient spending. Segmenting or grouping promotion areas can be addressed using data mining techniques, specifically clustering. This study aims to segment promotion areas using clustering algorithms, namely K-Means and K-Medoids, along with the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The evaluation of DBI (Davies-Bouldin Index) showed that the K-Means algorithm performed better than the K-Medoids algorithm. The comparison between the K-Means and K-Medoids algorithms was assessed based on the DBI evaluation results, with the smallest DBI value observed in the K-Means algorithm. The DBI value for K-Medoids was 0.196, while for K-Means it was 0.170.
Android-Based Detection Application Of Indonesian Sign Language System (SIBI) Using Rapid Application Development Method Sadr Lufti Mufreni; Tikaridha Hardiani; Muhammad Ircham Maulana
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.92

Abstract

Deaf people have limited hearing which causes them to use sign language as a medium of communication. This research continues the development of the object model into an Android-based application to detect gestures of the Indonesian Sign Language System (SIBI) and translate them into spoken language. This research uses the Rapid Application Development (RAD) Method, the RAD Method development process is carried out iteratively through the stages of requirements planning, user design, construction and completion, with the Kotlin programming language and the TensorFlow Lite-based gesture detection model. Testing is done with respondents who are people with normal hearing or lay people and using the Blackbox Testing method. This application consists of three main features, real-time SIBI motion detection, information about SIBI, and translation from Indonesian to SIBI sign language. The test results from 30 respondents showed the usability test of the MySIBI application reached 85.6% which means this application is feasible to use and the results of Black Box Testing with an accuracy value of 100% which means the functionality of this system is very high. This application was successfully developed as an effective communication tool, which can bridge communication between deaf people and the wider community in Indonesia.

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